Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari
{"title":"学习驾驭买入周期:下一篮子回购推荐的超卷积模型","authors":"Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari","doi":"10.1145/3523227.3546763","DOIUrl":null,"url":null,"abstract":"The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation\",\"authors\":\"Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari\",\"doi\":\"10.1145/3523227.3546763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.\",\"PeriodicalId\":443279,\"journal\":{\"name\":\"Proceedings of the 16th ACM Conference on Recommender Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3523227.3546763\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3523227.3546763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.